UFPE, 26 de agosto de 2020
Abertura: “Avoids intellectual, philosophical discussions.”
Conscienciosidade: “Is systematic, likes to keep things in order.”
Extroversão: “Is dominant, acts as a leader.”
Amabilidade: “Starts arguments with others.” (R)
Neuroticismo: “Can be tense.”
| Métodos | Exemplo |
|---|---|
| Análise de string longa | {1,1,1,1,1} ou {3,3,3,3,3} |
| Item de validade | “Por favor, selecione ‘Concordo’ para este item” |
| Índices de ajuste \(l_z\) | estatística de ajuste de pessoa (person-fit) (Sinharay, 2015) |
| Tempo de resposta | Tempo total para completar a avaliação |
Tempo de resposta por página (Soland et al., 2019)
Modelagem mista para tempo de resposta and acuidade de resposta (Wang & Xu, 2015)
Outros tipos de dados:
\[\iff logit(\pi_{ip}) = \theta_p - \beta_j\]
\[logit(\pi_{ip}) = \theta_p - \beta_j \text{ (modelo Rasch)}\]
\[logit(\pi_{ip}) = \theta_p - \Sigma_{k=0}^K \delta_k X_{ik} \text{, } \theta_{p} \sim N(0,\sigma_\theta^2)\]
\[\beta_j = \delta_0 + \Sigma_{k=1}^K \delta_k X_{ik}\]
\[log(t_{ip}) = \tau_{p} - \lambda_{i} + \epsilon_{ip} \]
Usando R e o pacote lme4 para análise, com user_id como efeito aleatório e item_name como efeito misto:
## lmer: linear mixed-effects via REML
lme4::lmer(log_time ~ -1 + item_name + (1|user_id),
data = bfi_long_rev)
Groups Name Variance Std.Dev.
user_id (Intercept) 0.2265 0.4759
Residual 0.5153 0.7178
Number of obs: 13110, groups: user_id, 215
| A01 | 1.08 | C01 | 1.04 | E01 | 0.87 | N01 | 1.18 | O01 | 1.25 |
| A02 | 1.12 | C02 | 0.97 | E02 | 1.18 | N02 | 1.31 | O02 | 1.11 |
| A03 | 1.31 | C03 | 1.00 | E03 | 1.42 | N03 | 1.23 | O03 | 1.27 |
| A04 | 1.17 | C04 | 1.16 | E04 | 1.02 | N04 | 0.97 | O04 | 1.01 |
| … | |||||||||
| A12 | 1.02 | C12 | 1.10 | E12 | 0.90 | N12 | 1.06 | O12 | 0.94 |
| \(\sigma_a =\) 0.11 | \(\bar{\lambda}_a =\) _1.08 | \(\sigma_c =\) 0.14 | \(\bar{\lambda}_c =\) 1.06 | \(\sigma_e =\) 0.20 | \(\bar{\lambda}_e =\) 1.04 | \(\sigma_n =\) 0.17 | \(\bar{\lambda}_n =\) 1.08 | \(\sigma_o =\) 0.15 | \(\bar{\lambda}_o =\) 1.09 |
## response_obs ## 1 2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,4,3,3,4,2,2,3,3,4,3,3,2,2,3,2,2,3,3,2,4,2,2,3,4,4,2,2,2,4,4,2,5,4,2,4,4,4,2,5,3,4,2,2,4,4,2,5,3,4,4,2,2,4,4,4 ## 2 4,4,3,4,4,2,3,3,4,4,4,2,1,3,3,5,5,3,2,2,4,4,2,3,3,4,2,2,2,4,4,3,4,2,2,3,3,4,1,4,2,2,4,5,5,3,2,2,4,2,1,1,4,4,1,5,2,4,5,5,2,2,4 ## 3 3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2 ## 4 1,1,1,1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 ## 5 4,3,3,3,4,4,4,4,2,4,4,4,4,4,3,3,3,3,4,4,3,4,3,3,3,3,3,4,4,4,3,4,4,4,4,4,4,2,2,2,4,4,3,3,3,4,5,5,5,5,5,3,3,3,1,2,2,5,5,2,5,5,2,2,5,5,5,5,5,5,2,5,5,5,5,5,5,2,2,2,5,3,5 ## 6 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 ## 7 3,2,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2 ## 8 2,2,4,4,5,5,4,1,5,4,4,2,2,1,4,4,1,5,2,5,4,2,5,4,2,1,2,4,4,5,2,4,4,5,5,5,5,4,1,4,3,5,4,5,4,4,1,1,1,1,1,4,5,5,3,3,5,5,5,5,4,1,3,5,5,3 ## 9 4,4,5,4,5,5,1,5,4,5,1,3,3,4,2,1,2,3,4,1,2,3,4,3,1,3,4,4,3,3,4,3,2,5,3,4,4,5,5,2,5,2,5,2,2,3,2,2,1,4,4,1,5,5,5,2,3,1,2,2,2,2,2,3,3 ## 10 1,2,3,2,1,2,1,1,4,1,5,2,4,5,4,1,4,5,5,2,4,4,4,2,2,5,5,4,4,2,4,3,3,4,3,3,5,3,3,3,1,1,5,3,5,3,1,4,3,3,5,1,5,3,5,3,5,4,1,3,3,2,1,3,1,5,5,3,3,5,5 ## 11 4,1,2,3,2,5,4,5,5,5,5,4,5,2,5,1,4,5,4,5,4,5,2,4,3,2,4,2,2,2,1,5,3,5,5,3,1,5,5,3,2,3,4,5,3,4,1,1,1,4,1,4,5,5,3,1,4,5,4,3,1 ## 12 1,3,1,4,4,2,3,5,5,4,3,1,4,3,2,3,4,2,1,4,3,4,4,5,4,3,4,5,3,4,3,3,5,2,2,5,2,4,5,5,2,5,4,5,5,2,3,5,5,5,2,2,5,2,4,2,2,5,4,2,2,4,4,2
## user_id total_time sim_obs n_clicks ## 1 1923 43.230 0.7605634 71 ## 2 1741 47.337 0.8571429 63 ## 3 1665 49.635 0.8769231 65 ## 4 1650 54.694 0.7746479 71 ## 5 1915 55.444 0.5180723 83 ## 6 1652 98.412 0.9677419 62 ## 7 1751 128.083 0.9375000 64 ## 8 1871 611.979 0.9242424 66 ## 9 1785 689.195 0.8615385 65 ## 10 1699 1004.400 0.8450704 71 ## 11 1696 1598.799 0.5901639 61 ## 12 1772 170756.261 0.9531250 64
\[log(t_{ip}) = \tau_p - \gamma_0 - \Sigma_{k=1}^K \gamma_k X_{ik} + \epsilon_{ip}\]
Usando R e o pacote lme4 para análise, com user_id como efeito aleatório e como efeitos fixos os preditores char_count, is_reverse e domain:
lme4::lmer(log_time ~ 1 + char_count + is_reverse + domain + (1|user_id),
data=bfi_long_rev)
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.6643354 0.0436132 15.232
char_count 0.0113509 0.0007908 14.353
is_reverseTRUE 0.1331708 0.0128670 10.350
domainc -0.0257613 0.0202618 -1.271
domaine 0.0196413 0.0206827 0.950
domainn 0.0138497 0.0202724 0.683
domaino -0.0200226 0.0203781 -0.983
Correlation of Fixed Effects:
(Intr) chr_cn i_TRUE domanc domaine domann
char_count -0.544
is_rvrsTRUE -0.097 -0.090
domainc -0.231 -0.003 0.000
domaine -0.337 0.201 -0.018 0.489
domainn -0.250 0.033 -0.003 0.500 0.496
domaino -0.173 -0.107 0.010 0.497 0.466 0.493
## Data: bfi_long_rev ## Models: ## item.explan: log_time ~ 1 + char_count + is_reverse + domain + (1 | user_id) ## descriptive: log_time ~ -1 + item_name + (1 | user_id) ## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq) ## item.explan 9 28354 28421 -14168 28336 ## descriptive 62 28249 28711 -14063 28125 210.42 53 < 2.2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Records every time a respondent choses an answer or modifies their answers
Output format is JSON and requires parsing in R
{"1":{"val":"QR~QID87~5","t":"2019-12-16T17:38:03.184Z"},
"2":{"val":"QR~QID65~28~2","t":"2019-12-16T17:38:14.369Z"}...